load packages

library(ggplot2)
library(readxl)
library(readr)
library(tidyverse)
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## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(sf)
## Warning: package 'sf' was built under R version 4.4.2
## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE
library(plotly)
## 
## Attaching package: 'plotly'
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##     layout
library(geojsonio)
## Warning: package 'geojsonio' was built under R version 4.4.2
## Registered S3 method overwritten by 'geojsonsf':
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##   print.geojson geojson
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## Attaching package: 'geojsonio'
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##     pretty

load dataset

df_descriptive=read_csv("filtered_merged_dataset_sample.csv")
## Rows: 10000 Columns: 20
## ── Column specification ──────────────────────────────────────
## Delimiter: ","
## chr   (8): BORO, PERP_AGE_GROUP, PERP_SEX, PERP_RACE, VIC_AGE_GROUP, VIC_SEX...
## dbl  (10): INCIDENT_KEY, PRECINCT, Latitude, Longitude, Number_poverty, Perc...
## date  (1): OCCUR_DATE
## time  (1): OCCUR_TIME
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
data_final <- read_csv("data_final.csv")
## Rows: 9820 Columns: 40
## ── Column specification ──────────────────────────────────────
## Delimiter: ","
## chr  (17): BORO, LOC_OF_OCCUR_DESC, LOC_CLASSFCTN_DESC, LOCATION_DESC, PERP_...
## dbl  (15): INCIDENT_KEY, PRECINCT, JURISDICTION_CODE, X_COORD_CD, Y_COORD_CD...
## num   (2): Number_poverty, Number_education
## lgl   (3): STATISTICAL_MURDER_FLAG, Is_Holiday, Sky_Is_Dark
## dttm  (1): OCCUR_DATETIME
## date  (1): OCCUR_DATE
## time  (1): OCCUR_TIME
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Plot of the number of incidents in each borough for each year

# Summarize data: count the number of incidents by borough and year
incident_summary <- df_descriptive %>%
  group_by(BORO, Year) %>%
  summarise(Number_of_Incidents = n(), .groups = "drop") %>%
  # Ensure missing years and boroughs are included
  complete(BORO, Year = full_seq(min(df_descriptive$Year):max(df_descriptive$Year), 1), fill = list(Number_of_Incidents = 0))

# Create the bar plot
ggplot(incident_summary, aes(x = Year, y = Number_of_Incidents, fill = BORO)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(
    title = "Number of Incidents in Each Borough by Year",
    x = "Year",
    y = "Number of Incidents",
    fill = "Borough"
  ) +
  scale_x_continuous(breaks = seq(min(df_descriptive$Year), max(df_descriptive$Year), by = 1)) +
  theme_minimal()

nta_summary <- df_descriptive %>%
  group_by(NTA_clean, BORO) %>%                 # Group by NTA and BORO
  summarise(Number_of_Incidents = n(), .groups = "drop") %>%  # Count incidents
  arrange(desc(Number_of_Incidents)) %>%  # Sort in descending order
  slice_head(n = 10)
nta_summary
## # A tibble: 10 × 3
##    NTA_clean          BORO      Number_of_Incidents
##    <chr>              <chr>                   <int>
##  1 east harlem        MANHATTAN                2690
##  2 bedford stuyvesant BROOKLYN                 1291
##  3 east new york      BROOKLYN                  734
##  4 brownsville        BROOKLYN                  651
##  5 bushwick           BROOKLYN                  612
##  6 jamaica            QUEENS                    442
##  7 sunset park        BROOKLYN                  385
##  8 flatbush           BROOKLYN                  197
##  9 washington heights MANHATTAN                 192
## 10 upper west side    MANHATTAN                 189

##Total incidents per NTA

Load spatial data (replace with actual shapefile path)

nta_shape <- st_read("nynta2020_24d/nynta2020.shp")
## Reading layer `nynta2020' from data source 
##   `D:\Rproject\ZebangZHANGstat.github.io\nynta2020_24d\nynta2020.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 262 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 913175.1 ymin: 120128.4 xmax: 1067383 ymax: 272844.3
## Projected CRS: NAD83 / New York Long Island (ftUS)
cdta_shape = st_read("nycdta2020_24d/nycdta2020.shp")
## Reading layer `nycdta2020' from data source 
##   `D:\Rproject\ZebangZHANGstat.github.io\nycdta2020_24d\nycdta2020.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 71 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 913175.1 ymin: 120128.4 xmax: 1067383 ymax: 272844.3
## Projected CRS: NAD83 / New York Long Island (ftUS)
boro_shape = st_read("Borough Boundaries/geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e.shp")
## Reading layer `geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e' from data source `D:\Rproject\ZebangZHANGstat.github.io\Borough Boundaries\geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84
# Prepare incident data: count incidents per NTA_clean
nta_incident_counts <- data_final %>%
  group_by(NTA) %>%
  summarise(Number_of_Incidents = n(), .groups = "drop")

# Merge spatial data with incident counts
nta_map_data <- nta_shape %>%
  left_join(nta_incident_counts, by = c("NTAName" = "NTA"))

# Create custom breaks for Number_of_Incidents
nta_map_data <- nta_map_data %>%
  mutate(
    Incident_Range = cut(
      Number_of_Incidents,
      breaks = seq(0, 400, by = 80),  # Breaks from 0 to 1000, every 200 cases
      labels = c("0-80", "81-160", "161-240", "241-320", "321-400"),
      include.lowest = TRUE
    )
  )

Plot the map

# Plot the map with custom ranges
ggplot(data = nta_map_data) +
  geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
  scale_fill_manual(
    values = c(
      "0-80" = "#b2e2e2",
      "81-160" = "skyblue",
      "161-240" = "#66c2a4",
      "241-320" = "#2ca25f",
      "321-400" = "#006d2c"
    ),
    name = "Number of Incidents"
  ) +
  labs(
    title = "Total Number of Incidents Across NYC NTAs from 2017 to 2023",
    subtitle = "Incidents grouped by range (0-1000, 200 breaks)",
    caption = "Data Source: Your dataset"
  ) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank()
  )

summarize CDTA and BORO

## There is space between letter and number in CDTA, I deleted the space below
data_final$CDTA <- gsub(" ", "", data_final$CDTA)

cdta_incident_counts <- data_final %>%
  group_by(CDTA) %>%
  summarise(Number_of_Incidents = n(), .groups = "drop")

merge datasets

# Prepare incident data: count incidents per NTA_clean
cdta_incident_counts <- data_final %>%
  group_by(CDTA) %>%
  summarise(Number_of_Incidents = n(), .groups = "drop")

# Merge spatial data with incident counts
cdta_map_data <- cdta_shape %>%
  left_join(cdta_incident_counts, by = c("CDTA2020" = "CDTA"))

# Create custom breaks for Number_of_Incidents
cdta_map_data <- cdta_map_data %>%
  mutate(
    Incident_Range = cut(
      Number_of_Incidents,
      breaks = seq(0, 400, by = 80),  # Breaks from 0 to 1000, every 200 cases
      labels = c("0-80", "81-160", "161-240", "241-320", "321-400"),
      include.lowest = TRUE
    )
  )
# Plot the map with custom ranges
ggplot(data = cdta_map_data) +
  geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
  geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") +  # Add labels
  scale_fill_manual(
    values = c(
      "0-80" = "#b2e2e2",
      "81-160" = "skyblue",
      "161-240" = "#66c2a4",
      "241-320" = "#2ca25f",
      "321-400" = "#006d2c"
    ),
    name = "Number of Incidents"
  ) +
  labs(
    title = "Total Number of Incidents Across NYC CDTAs from 2017 to 2023",
    subtitle = "Incidents grouped by range (0-400, 80 breaks)",
    caption = "Data Source: Your dataset"
  ) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank(), 
    axis.title.x = element_blank(),  # Remove x-axis label
    axis.title.y = element_blank()
  )
## Warning: Removed 3 rows containing missing values or values outside
## the scale range (`geom_text()`).

# Count the boro incident
boro_incident_counts <- data_final %>%
  group_by(BORO) %>%
  summarise(Number_of_Incidents = n(), .groups = "drop")  %>%
  mutate(BORO = tolower(BORO) )

# Lowercase the boro in boro_shape
boro_shape = boro_shape %>%
  mutate(boro_name = tolower(boro_name))
 

# Merge spatial data with incident counts
boro_map_data <- boro_shape %>%
  left_join(boro_incident_counts, by = c("boro_name" = "BORO"))

# Create custom breaks for Number_of_Incidents
boro_map_data <- boro_map_data %>%
  mutate(
    Incident_Range = cut(
      Number_of_Incidents,
      breaks = seq(0, 4000, by = 800),  # Breaks from 0 to 4000, every 800 cases
      labels = c("0-800", "801-1600", "1601-2400", "2401-3200", "3201-4000"),
      include.lowest = TRUE
    )
  )
# Plot the map with custom ranges
ggplot(data = boro_map_data) +
  geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
  geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") +  # Add labels
  scale_fill_manual(
    values = c(
      "0-800" = "#b2e2e2",
      "801-1600" = "skyblue",
      "1601-2400" = "#66c2a4",
      "2401-3200" = "#2ca25f",
      "3201-4000" = "#006d2c"
    ),
    name = "Number of Incidents"
  ) +
  labs(
    title = "Total Number of Incidents Across NYC BOROs from 2017 to 2023",
    subtitle = "Incidents grouped by range (0-4000, 800 breaks)",
    caption = "Data Source: Your dataset"
  ) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank(),
    axis.title.x = element_blank(),  # Remove x-axis label
    axis.title.y = element_blank()
  )
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

boro_map_data <- boro_map_data %>%
  mutate(hover_text = paste("Borough:", boro_name,
                            "<br>Number of Incidents:", Number_of_Incidents))
# Interactive plot using plotly
plot <- plot_ly() %>%
  add_sf(
    data = boro_map_data,
    split = ~boro_name,  # Separate polygons by boroughs
    color = ~Number_of_Incidents,  # Color based on the number of incidents
    colors = "viridis",  # Color palette
    text = ~hover_text,  # Hover text
    hoverinfo = "text"
  ) %>%
  layout(
    title = "Total Number of Incidents Across NYC BOROs (2017-2023)",
    geo = list(
      resolution = 50,
      showland = TRUE,
      landcolor = "rgb(217, 217, 217)",
      showlakes = TRUE,
      lakecolor = "rgb(173, 216, 230)",
      projection = list(type = "mercator")
    )
  )

# Display the plot
plot
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter